{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a8bc46a",
   "metadata": {},
   "source": [
    "# Introduction To AntiBody Drug-Conjugates **(ADCs)** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a34f53",
   "metadata": {},
   "source": [
    "In this tutorial we will explore the fundamental concepts of Antibody-Drug Conjugates(ADCs), with a focus on the key properties \n",
    "that play a crucial role in the design and development of ADCs.\n",
    "\n",
    "This tutorial is made to run without any GPU support, and can be used in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/Introduction_to_Antibody_Drug_Conjugates.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3694bb",
   "metadata": {},
   "source": [
    "This tutorial is divided into following sections - \n",
    "\n",
    "## Table of Contents\n",
    "* [Introduction](#introduction)\n",
    "* [Background Literature](#background-literature)\n",
    "* [Fundamental Concepts of ADCs](#fundamental-concepts)\n",
    "* [ADC Design and Optimization](#adc-design)\n",
    "* [Computational Approaches for ADC Analysis](#computational-approaches)\n",
    "* [References](#references)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e759aa0e",
   "metadata": {},
   "source": [
    "## Introduction <a name=\"introduction\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c442a40",
   "metadata": {},
   "source": [
    "### ADCs - what they are\n",
    "\n",
    "Antibody-Drug Conjugates or (ADCs) are a class of biopharmaceuticals designed for targeted cancer therapy, delivering potent cytotoxic (cell-killing) drugs directly to tumor cells while minimizing harm to healthy tissues. This targeted approach helps reduce side effects compared to traditional chemotherapy approach. \n",
    "\n",
    "ADCs are typically composed of a monoclonal antibody (mAbs) covalently attached to a cytotoxic drug via a chemical linker. It combines both the advantages of highly specific targeting ability and highly potent killing effect to achieve accurate and efficient elimination of cancer cells, which has become one of the hotspots for the research and development of anticancer drugs. [[1]](#1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2976c24",
   "metadata": {},
   "source": [
    "## Background Literature <a name=\"background-literature\"></a>\n",
    "\n",
    "Traditionally, the foundation of cancer treatment has been cytotoxic chemotherapy, surgery, and radiation therapy. However, in recent years, with the advent of immunotherapy and the ability to target specific molecular agents, the treatment of cancer has been revolutionized and is no longer limited only to the conventional chemotherapeutic agents. Traditional cancer chemotherapy, while effective in targeting rapidly dividing cells, often causes significant systemic toxicity due to its lack of selectivity, affecting both cancerous and healthy tissues. Antibody Drug Conjugates(ADCs) offer a promising alternative by specifically targeting tumor-associated antigens, enabling selective delivery of therapeutic agents to cancer cells.\n",
    "\n",
    "Currently, there are eleven FDA-approved ADCs available in the market. Furthermore, over 100 ADCs are in the diverse stages of development in clinical trials at the present time. Notably, a few novel ADCs have established substantial anti-tumor activity in many tumor types that share the expression of the targeted antigen, thus establishing a histology-agnostic activity of these compounds. For example, HER2 overexpressing breast, gastric and colorectal cancers, and Her-2 mutated lung cancers have demonstrated a significant response to the anti-Her2-targeting ADC, trastuzumab deruxtecan. [[2]](#2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a3a67a",
   "metadata": {},
   "source": [
    "### Key Components of ADC\n",
    "\n",
    "An antibody-drug conjugate (ADC) is composed of three primary components:\n",
    "\n",
    "- **Monoclonal Antibody(mAb)**: Antibody used in ADCs should be specific for a tumor-associated antigen that has restricted expression on normal cells and overexpressed on cancer cells. This allows the ADC to selectively target cancer cells while sparing normal cells.\n",
    "\n",
    "- **Cytotoxic drug payload**: Payload is designed to kill target cells when internalized and released. These payloads are often drugs that are highly toxic and require antibodies to specifically target cancer cells so they do not affect healthy cells. \n",
    "\n",
    "- **Linker molecule**: A chemical bridge linking the mAb and payload, stable in circulation but designed to release the drug in tumor cells. Linkers (cleavable, e.g., Adcetris, or non-cleavable, e.g., Kadcyla) influence ADC stability, solubility, and toxicity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5975275",
   "metadata": {},
   "source": [
    "<img src=\"assets/adc_components.png\" alt=\"image.png\" height=\"400\" width=\"900\">\n",
    "\n",
    "---\n",
    "\n",
    "**Fig.1** : Schematic description of an ADC. The core components including target antigen, antibody, linker, cytotoxic drug along with their key functions are demonstrated. [[3]](#3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9769aff3",
   "metadata": {},
   "source": [
    "### Why Are ADCs Essential ?\n",
    "\n",
    "ADCs revolutionize cancer treatment in many ways. Some of them are: \n",
    "\n",
    "- **Targeted Therapy**: Unlike traditional chemotherapy, which indiscriminately targets rapidly dividing cells and causes significant toxicity, ADCs selectively deliver cytotoxic (cell-killing) payloads to cancer cells, sparing healthy tissues and improving patient outcomes .\n",
    "\n",
    "- **Enhanced Therapeutic Index**: By focusing drug action on tumor cells, ADCs achieve higher efficacy at lower doses, reducing systemic side effects compared to conventional chemotherapies. Approved ADCs like Brentuximab Vedotin and Trastuzumab Emtansine have demonstrated significant clinical success in treating lymphomas and breast cancer, respectively.\n",
    "\n",
    "- **Versatility Across Cancers**: ADCs can target a wide range of tumor-associated antigens, making them applicable to various cancers, including those resistant to standard treatments. \n",
    "\n",
    "- **Overcoming Resistance**: ADCs can address drug resistance by delivering novel payloads with distinct mechanisms of action, such as DNA-damaging agents or tubulin inhibitors, bypassing resistance pathways in cancer cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4da6d0",
   "metadata": {},
   "source": [
    "## Fundamental Concepts of ADC <a name=\"fundamental-concepts\"></a>\n",
    "\n",
    "Here we cover some properties of the key components of ADC, and mechanism of action along with the setbacks involved in the development."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27da513c",
   "metadata": {},
   "source": [
    "### 2.1 Choice of Monoclonal antibodies:\n",
    "Monoclonal antibodies (mAbs) are the guiding component of Antibody-Drug Conjugates (ADCs), acting like a GPS to deliver the drug directly to cancer cells. They are designed to recognize specific markers, called antigens, found mostly on cancer cells, such as HER2 in breast cancer or CD30 in lymphomas, while avoiding healthy cells. This selective targeting helps ADCs attack tumors without harming the rest of the body.\n",
    "\n",
    " An ideal mAb has three key features: \n",
    "  - High specificity : It binds strongly to the cancer cell’s antigen.\n",
    "  - Efficient internalization : Gets absorbed quickly into the tumor cell to deliver the drug.\n",
    "  - Low immunogenicity : Causes minimal immune reactions to keep the treatment safe. \n",
    "\n",
    "For example : trastuzumab in Kadcyla targets HER2 on breast cancer cells, and brentuximab in Adcetris targets CD30 on lymphoma cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f4dc1a",
   "metadata": {},
   "source": [
    "### 2.2 Importance of Cytotoxic Payloads: \n",
    "The ADCs payloads play a key role in determining the efficacy of ADC drugs and thus have attracted great attention in the field. An ideal payload must be highly potent to destroy cancer cells, stable in the bloodstream to avoid premature release, and chemically adaptable to connect with the antibody via a linker . It should also have low immunogenicity to prevent unwanted immune reactions and proper solubility to ensure the ADC dissolves well in the body . \n",
    "\n",
    "Some payloads have a \"bystander effect,\" meaning they can kill nearby cancer cells, even those without the target marker, which is useful for tumors with uneven marker expression . However, payloads must be carefully designed to avoid toxicity to healthy tissues. [[4]](#4)\n",
    "\n",
    "#### 2.2.1 Classes of Payloads: \n",
    "ADC payloads are grouped into three main types based on how they attack cancer cells. Each type works differently to stop cancer cells from growing or surviving.\n",
    "| Class                     | Mechanism of Action                                              | Examples                                   |\n",
    "|---------------------------|-----------------------------------------------------------------|--------------------------------------------|\n",
    "| Tubulin Inhibitors        | Disrupt microtubule dynamics, causing mitotic arrest and cell death | Auristatins (MMAE, MMAF), Maytansinoids (DM1, DM4), Tubulysins |\n",
    "| DNA Damaging Agents       | Induce DNA strand breaks or alkylate DNA, leading to apoptosis  | Calicheamicin, Duocarmycins, Pyrrolobenzodiazepines (PBDs)     |\n",
    "| Topoisomerase I Inhibitors| Inhibit topoisomerase I, preventing DNA replication and transcription | Deruxtecan (DXd), SN-38                    |\n",
    "\n",
    "Common ADC payloads include tubulin inhibitors and DNA damaging agents, with tubulin inhibitors accounting for more than half of the ADC drugs in clinical development. [[5]](#5)\n",
    "\n",
    "\n",
    "#### 2.2.2 Mechanism of Cytotoxicity:\n",
    "\n",
    "The mechanism of action of the payload is an important factor determining the performance of the ADC (For e.g., Adverse Reactions). Once an ADC is administered, it circulates in the bloodstream until it binds to its specific antigen on the surface of a tumor cell. This binding triggers the internalization of the ADC–antigen complex, which is then transported to the lysosome inside the cell. Within the lysosome, the complex is degraded, and the cytotoxic payload is released. <br>\n",
    "\n",
    "The released payload then acts on its intracellular target-such as microtubules, DNA, or RNA-disrupting essential cellular functions and inducing cell death. Some payloads are designed to have a bystander effect, meaning they can diffuse out of the targeted cell and kill neighboring cancer cells that may not express the target antigen. This is especially useful in tumors where antigen expression is uneven. However, achieving this effect requires a careful balance, as highly membrane-permeable payloads can also increase toxicity to healthy tissues."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86fc065a",
   "metadata": {},
   "source": [
    "### 2.3 Design and Role of Linkers: \n",
    "\n",
    "Linker in ADC bridges the antibody with the cytotoxic drug. An ideal linker should not induce ADC aggregation, and it is expected to limit premature release of payloads in plasma and to promote active drugs release at desired targeted sites. Depending on the metabolic fate in cells, two types of linkers including cleavable and non-cleavable linkers have been employed in most of ADC drugs. [[6]](#6) <br>\n",
    "\n",
    "Types of Linkers : \n",
    "\n",
    "#### Cleavable Linkers:\n",
    "\n",
    "The first type is the cleavable linkers, which has a chemical trigger in its structure that can be efficiently cleaved to release the cytotoxic payload in the tumor. They are engineered to remain stable in the bloodstream but to release the payload efficiently once inside the tumor environment. This release is triggered by specific conditions unique to tumors, such as acidic pH, elevated levels of certain enzymes, or a reductive environment. These linkers often contain chemical triggers that respond to these cues, ensuring that the cytotoxic drug is delivered primarily to cancer cells. \n",
    "\n",
    "More than 80% of the clinically approved ADCs employ cleavable linkers such as,  inotuzumab ozogamicin (Besponsa) and brentuximab vedotin (Adcetris).\n",
    "\n",
    "#### Non-Cleavable Linkers:\n",
    "\n",
    " The other type of linker is noncleavable. In contrast to the cleavable linker, there are no chemical triggers in this structure, and the linker is part of the payload. This type of linker has been employed only in ado-trastuzumab emtansine (Kadcyla, T-DM1) among the approved ADCs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cba5bf4d",
   "metadata": {},
   "source": [
    "#### Challenges involved in Linker Design:\n",
    "\n",
    "Although ADCs have made remarkable strides in targeted cancer therapy, the continued advancement of this technology is increasingly limited by the challenges associated with linker design.\n",
    "\n",
    "Before we move further, it is essential to know some of the challenges involved in Linker Design mentioned here -  \n",
    "\n",
    "- Premature release : Many classical linkers are prone to nonspecific payload release in non-tumorous tissues, which can cause off-target toxicity and restrict the therapeutic window. A prominent example is gemtuzumab ozogamicin (Mylotarg), which was withdrawn from the market in 2010 after reports of severe liver toxicity linked to its unstable N-acylhydrazone linker. Although Mylotarg was later re-approved in 2017 following a redesign, the FDA required a black-box warning due to the continued risk of liver toxicity. <br>\n",
    "\n",
    "- Secondly, the commonly used maleimide-based linkers, such as succinimidyl 4-(N-maleimidomethyl) cyclohexane-1-carboxylate (SMCC), are susceptible to retro-Michael elimination reactions. This instability can lead to a significant reduction in ADC efficacy over time. For example, studies have shown that the SMCC linker degrades to just 38% of its original form after 120 hours in mouse plasma, and Kadcyla, which contains an SMCC linker, exhibited a 29% decrease in drug-to-antibody ratio (DAR) after seven days in mice. <br>\n",
    "\n",
    "- Thirdly, the limited options for linker–payload attachment have become a bottleneck, especially as the field rapidly expands to include novel payloads for cancer treatment, antimicrobial therapy, and immune modulation. Many innovative payloads are awaiting suitable linker technologies to unlock their therapeutic potential.\n",
    "\n",
    "In response to these challenges, the past five years have seen significant progress in linker design. Key developments include the optimization of existing chemical triggers and the creation of novel triggers for highly selective linkers, the invention of new linker–antibody attachment strategies to produce more stable and homogeneous ADCs, the expansion of linker–payload attachment options to accommodate a wider range of payloads, and the refinement of linker properties to improve the absorption, distribution, metabolism, and excretion (ADME) profiles of ADCs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d0aaf3",
   "metadata": {},
   "source": [
    "### 2.4 Understanding and Measuring the Drug-Antibody Ratio (DAR) in ADCs:\n",
    "\n",
    "The Drug-Antibody Ratio (DAR) measures how many drug molecules are attached to each antibody in an ADC; theoretically, it should characterize the ability of an ADC to deliver drug to a tumor and thus positively correlate to effectiveness.\n",
    "\n",
    "#### 2.4.1 Why DAR Matters:\n",
    "\n",
    "DAR is more than just a number. A higher DAR means more drug molecules, potentially increasing tumor-killing potential, but it comes with trade-offs such as:\n",
    "\n",
    "- **Aggregation Risk**: Lipophilic drugs and linkers, common in high-DAR ADCs, increase hydrophobicity, leading to aggregation that prevents ADCs from reaching their target.\n",
    "\n",
    "- **Binding Interference**: Excessive drug loading (e.g., DAR ≥ 8) can sterically block antigen-binding sites, reducing specificity and activity.\n",
    "\n",
    "- **Toxicity and Clearance**: High DAR is linked to significant toxicity and faster plasma clearance, shortening residence time and limiting efficacy.\n",
    "\n",
    "- **Consistent DAR**: Producing ADCs with uniform DAR ensures predictable biological activity, making it a critical attribute for manufacturing and therapeutic success.\n",
    "\n",
    "##### 2.4.2 Measuring DAR:\n",
    "\n",
    "DAR is usually determined using laboratory techniques like mass spectrometry or UV spectrophotometry. These methods compare the amount of drug and antibody in a sample to calculate the average DAR.\n",
    "\n",
    "The most common formula for calculating average DAR using chromatographic peak areas is [[7]](#7):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f2038b",
   "metadata": {},
   "source": [
    "$$\n",
    "\\text{DAR} = \\frac{\\sum_{i=0}^{n} (i \\times P_i)}{\\sum_{i=0}^{n} P_i}\n",
    "$$\n",
    "\n",
    "where:\n",
    "\n",
    "- $i$ is the number of drugs attached\n",
    "- $P_i$ is the percentage or area of the peak corresponding to species with $i$ drugs attached"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7644ed10",
   "metadata": {},
   "source": [
    "Secondly, UV-based analysis using ultraviolet–visible (UV-Vis) spectrophotometry is one of the convenient methods for determining the DAR value. According to the Beer–Lambert Law, absorbance is directly proportional to the concentration of a substance in solution. The relationship is given by the equation [[8]](#8):\n",
    "\n",
    "$$\n",
    "A = \\varepsilon cl\n",
    "$$\n",
    "\n",
    "where A is the absorbance,\n",
    "\n",
    "ε is the molar absorptivity (a physical constant related to the amino acid composition of the substance),\n",
    "\n",
    "l is the path length of the cuvette (usually 1 cm or 1 mm),\n",
    "\n",
    "c is the concentration. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "116094a9",
   "metadata": {},
   "source": [
    "In this approach, absorbance is measured at two distinct wavelengths, and simultaneous equations are established using the known extinction coefficients of the antibody and the drug to calculate the average drug-to-antibody ratio."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc380b26",
   "metadata": {},
   "source": [
    "$$\n",
    "A_{280} = \\varepsilon_{A,280} C_A + \\varepsilon_{D,280} C_D\n",
    "$$\n",
    "\n",
    "$$\n",
    "A_{\\lambda(D)} = \\varepsilon_{A,\\lambda(D)} C_A + \\varepsilon_{D,\\lambda(D)} C_D\n",
    "$$\n",
    "\n",
    "where:\n",
    "\n",
    "- $A_{280}$ and $A_{\\lambda(D)}$ are absorbances at 280 nm and at the drug’s maximum absorption wavelength, respectively  \n",
    "- $\\varepsilon$ are extinction coefficients  \n",
    "- $C_A$ and $C_D$ are concentrations of antibody and drug\n",
    "\n",
    "Solving for $C_A$ and $C_D$, then:\n",
    "\n",
    "$$\n",
    "\\text{DAR} = \\frac{C_D}{C_A}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964f9653",
   "metadata": {},
   "source": [
    "### 2.5 Mechanism of action of ADCs : \n",
    "\n",
    "Let's learn about the core mechanism by which ADCs function -\n",
    "\n",
    "<img src=\"assets/mechanism_of_adc_action.png\" width= \"800\" height=\"400\"></img>\n",
    "\n",
    "**Fig.2** : Schematic description of mechanism of action of an ADC. The core components including target antigen, antibody, linker, cytotoxic drug along with their key functions are demonstrated. [[2]](#2)\n",
    "\n",
    "(1) Targeted Binding : The antibody part of the ADC sticks to a specific antigen on the cancer cell’s surface, like a key fitting into a lock. This antigen is usually more common on cancer cells, making the ADC highly selective.\n",
    "\n",
    "(2) Cell Uptake: The cancer cell swallows the ADC in a process called endocytosis, pulling it into an endosome that fuses with a lysosome.\n",
    "\n",
    "(3) Antibody Degradation: Inside the lysosome, enzymes chop up the ADC, breaking the link between the antibody and the toxic payload.\n",
    "\n",
    "(4) Payload Release: The freed toxic drug escapes the lysosome and enters the cancer cell’s cytoplasm or nucleus, ready to cause damage.\n",
    "\n",
    "(5) DNA or Microtubule Disruption: The payload attacks either the cell’s DNA (its genetic blueprint) or microtubules (its structural supports), stopping the cell from dividing.\n",
    "\n",
    "(6) Cell Death: The damage overwhelms the cancer cell, leading to its destruction through processes like programmed cell death."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fdaab0",
   "metadata": {},
   "source": [
    "## 3.1 ADC Design and Optimisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3493458d",
   "metadata": {},
   "source": [
    "Designing effective ADCs involves several critical considerations to ensure optimal therapeutic efficacy and safety.\n",
    "\n",
    "### Target Antigen Selection : \n",
    "\n",
    "The first step in designing an ADC is selecting the right target antigen. Ideal antigens exhibit high expression on tumor cells, minimal or no expression on healthy tissues, and low shedding rates.\n",
    "Therefore, targeting these antigens minimizes off-target effects and enhances the ADC’s specificity. Moreover, a uniform distribution of antigens across tumor cells ensures efficient binding and internalization of the ADC, which is essential for delivering the payload effectively.\n",
    "\n",
    "### Antibody Engineering : \n",
    "\n",
    "The antibody component is responsible for recognizing and binding to the selected antigen. Monoclonal antibodies (mAbs) are used, and are often humanized or fully human to reduce immunogenic responses.\n",
    "So, the risk of the body recognizing the antibody as foreign and launching an immune response is minimized. Additionally, affinity must be carefully balanced—too high, and the antibody might not penetrate deep into the tumor; too low, and binding may be insufficient. Hence, fine-tuning the antibody’s properties is crucial to ensure it reaches and binds to all parts of the tumor.\n",
    "\n",
    "### Payload Selection :\n",
    " \n",
    "Cytotoxic payloads kill tumor cells. Since only a small amount of ADC reaches the tumor, the payload must be effective at very low concentrations (picomolar levels). Common types include microtubule inhibitors (e.g., auristatins, maytansinoids), DNA-damaging agents (e.g., calicheamicin, duocarmycins), and topoisomerase inhibitors (e.g., SN-38). Therefore, the payload is selected based on potency, stability, solubility, and whether bystander killing is desirable (i.e., killing neighboring tumor cells that may not express the antigen).\n",
    "\n",
    "### Linker Design\n",
    "\n",
    "Linkers connect antibodies to payloads and must be stable in the bloodstream but cleavable inside the target cell. Recent advancements like hydrophilic linkers (e.g., PEGylated linkers) have improved solubility and reduced aggregation. As a result, these linkers enhance pharmacokinetics and decrease off-target toxicity, making the ADC safer and more effective.\n",
    "\n",
    "### Conjugation Methods : \n",
    "\n",
    "The way the payload is attached to the antibody affects the Drug-Antibody Ratio (DAR) and the uniformity of the ADC. Traditional conjugation methods (e.g., random attachment to lysine or cysteine residues) often produce heterogeneous mixtures with variable DARs. This inconsistency can affect the ADC’s efficacy and safety profile.\n",
    "\n",
    "Therefore, site-specific conjugation techniques, such as engineered cysteine residues or enzymatic methods have been developed. These methods allow for precise, reproducible attachment points, resulting in homogeneous ADCs with defined DARs. Hence, site-specific conjugation significantly improves the therapeutic outcome and consistency of ADC products."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ebd6a4",
   "metadata": {},
   "source": [
    "## 4.1 Computational Approaches for ADC Analysis <a name= \"computational-approaches\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097c8880",
   "metadata": {},
   "source": [
    "#### Traditional approach in payload prediction:\n",
    "Traditionally, the selection and optimization of ADC payloads have depended on empirical, trial-and-error methods such as high-throughput screening and structure-activity relationship (SAR) studies. While these approaches have led to the development of several approved ADCs, they are time-consuming, resource-intensive, and often inefficient in exploring the vast chemical space. Additionally, the complex interplay between payload structure, linker chemistry, and antibody properties introduces further difficulty in using conventional predictive strategies.\n",
    "To address these challenges, there is growing momentum toward leveraging advanced computational techniques, particularly machine learning to predict ADC payload activity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2da2c43d",
   "metadata": {},
   "source": [
    "### How can we use Machine learning ?\n",
    "\n",
    "The limitations of traditional methods for predicting ADC (Antibody-Drug Conjugate) activity have sparked increasing interest in machine learning (ML) approaches for molecular property prediction.In this section, we focus specifically on ADC payload activity prediction. But before diving into the details, it's important to understand why this aspect is so significant.\n",
    "\n",
    "The therapeutic effectiveness of ADCs fundamentally relies on three key components: the antibody, the linker, and the payload. While each plays a vital role in the overall function, the payload (typically a highly potent small-molecule cytotoxin) is especially critical in determining the efficacy of the ADC. For an ADC to be successful, the payload must demonstrate not only strong cytotoxic activity but also favorable physicochemical properties that allow for stable conjugation, efficient release, and an effective mechanism of action against target cancer cells. Because of this, the rational design, careful selection, and precise optimization of payloads have become major challenges in ADC development."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458bd56d",
   "metadata": {},
   "source": [
    "Before we proceed, let's install deepchem into our environment and setup other required dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fe75e50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: deepchem in /home/sonali/Downloads/deepchemlatest/deepchem (2.8.1.dev20250316001040)\n",
      "Requirement already satisfied: py3Dmol in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (2.4.2)\n",
      "Requirement already satisfied: rdkit in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (2024.3.5)\n",
      "Requirement already satisfied: joblib in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from deepchem) (1.4.2)\n",
      "Requirement already satisfied: numpy<2 in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from deepchem) (1.24.3)\n",
      "Requirement already satisfied: pandas in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from deepchem) (2.0.3)\n",
      "Requirement already satisfied: scikit-learn in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from deepchem) (1.3.2)\n",
      "Requirement already satisfied: sympy in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from deepchem) (1.13.3)\n",
      "Requirement already satisfied: scipy>=1.10.1 in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from deepchem) (1.10.1)\n",
      "Requirement already satisfied: Pillow in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from rdkit) (10.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from pandas->deepchem) (2.9.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from pandas->deepchem) (2025.1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from pandas->deepchem) (2025.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from scikit-learn->deepchem) (3.5.0)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from sympy->deepchem) (1.3.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas->deepchem) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No normalization for SPS. Feature removed!\n",
      "No normalization for AvgIpc. Feature removed!\n",
      "/home/sonali/anaconda3/envs/deepchem/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "2025-07-03 16:37:00.968552: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-07-03 16:37:01.081546: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-07-03 16:37:01.412357: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2025-07-03 16:37:03.506590: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "Skipped loading modules with pytorch-geometric dependency, missing a dependency. No module named 'dgl'\n",
      "Skipped loading modules with pytorch-geometric dependency, missing a dependency. cannot import name 'MXMNet' from 'deepchem.models.torch_models' (/home/sonali/Downloads/deepchemlatest/deepchem/deepchem/models/torch_models/__init__.py)\n",
      "Skipped loading some Jax models, missing a dependency. jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.\n"
     ]
    }
   ],
   "source": [
    "!pip install deepchem py3Dmol rdkit\n",
    "\n",
    "import deepchem as dc\n",
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw, AllChem\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0070dd53",
   "metadata": {},
   "source": [
    "Let’s first take a look at a few of the approved ADCs and their key properties.\n",
    "\n",
    "Below is a dataset containing the approved ADCs and their core properties. For a deeper dive into structural, mechanistic, and pharmacological details, explore the full listings on [[9]](#9)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09952f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to dataset present in assets\n",
    "file_path = \"assets/approved_adc.csv\"\n",
    "\n",
    "# Load the dataset into a pandas DataFrame\n",
    "df = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01203188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>ADC name</th>\n",
       "      <th>Brand name</th>\n",
       "      <th>Company</th>\n",
       "      <th>Target</th>\n",
       "      <th>Payload</th>\n",
       "      <th>Antibody</th>\n",
       "      <th>Indication</th>\n",
       "      <th>Payload SMILES</th>\n",
       "      <th>Launch year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Gemtuzumab ozogamicin (GO)</td>\n",
       "      <td>Mylotarg</td>\n",
       "      <td>Pfizer/Wyeth</td>\n",
       "      <td>CD33</td>\n",
       "      <td>Calicheamicin</td>\n",
       "      <td>Gemtuzumab</td>\n",
       "      <td>Acute myeloid leukemia</td>\n",
       "      <td>CCN(C(C)=O)[C@H]1CO[C@@H](O[C@@H]2[C@@H](O)[C@...</td>\n",
       "      <td>2000.05\\r\\nRelaunched in 2017.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Brentuximab vedotin (SGN-35)</td>\n",
       "      <td>Adcetris</td>\n",
       "      <td>Seattle Genetics Millennium/Takeda</td>\n",
       "      <td>CD30</td>\n",
       "      <td>MMAE</td>\n",
       "      <td>Brentuximab</td>\n",
       "      <td>Hodgkin lymphoma, large cell lymphoma</td>\n",
       "      <td>CC[C@H](C)[C@@H]([C@@H](CC(=O)N1CCC[C@H]1[C@@H...</td>\n",
       "      <td>2011.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Trastuzumab emtansine (T-DM1)</td>\n",
       "      <td>Kadcyla</td>\n",
       "      <td>Genentech/Roche</td>\n",
       "      <td>HER2</td>\n",
       "      <td>DM1</td>\n",
       "      <td>Trastuzumab</td>\n",
       "      <td>HER2+ breast cancer</td>\n",
       "      <td>C[C@@H]1[C@@H]2C[C@]([C@@H](/C=C/C=C(/CC3=CC(=...</td>\n",
       "      <td>2013.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Inotuzumab ozogamicin</td>\n",
       "      <td>Besponsa</td>\n",
       "      <td>Pfizer/Wyeth</td>\n",
       "      <td>CD22</td>\n",
       "      <td>Calicheamicin</td>\n",
       "      <td>Inotuzumab</td>\n",
       "      <td>Acute lymphoblastic leukemia</td>\n",
       "      <td>CCN(C(C)=O)[C@H]1CO[C@@H](O[C@@H]2[C@@H](O)[C@...</td>\n",
       "      <td>2017.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Moxetumomab pasudotox</td>\n",
       "      <td>Lumoxiti</td>\n",
       "      <td>AstraZeneca</td>\n",
       "      <td>CD22</td>\n",
       "      <td>Pseudomonas exotoxin</td>\n",
       "      <td>Moxetumomab</td>\n",
       "      <td>Relapsed or refractory hairy cell leukemia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.                       ADC name Brand name  \\\n",
       "0    1     Gemtuzumab ozogamicin (GO)   Mylotarg   \n",
       "1    2   Brentuximab vedotin (SGN-35)   Adcetris   \n",
       "2    3  Trastuzumab emtansine (T-DM1)    Kadcyla   \n",
       "3    4          Inotuzumab ozogamicin   Besponsa   \n",
       "4    5          Moxetumomab pasudotox   Lumoxiti   \n",
       "\n",
       "                              Company Target               Payload  \\\n",
       "0                        Pfizer/Wyeth   CD33         Calicheamicin   \n",
       "1  Seattle Genetics Millennium/Takeda   CD30                  MMAE   \n",
       "2                     Genentech/Roche   HER2                   DM1   \n",
       "3                        Pfizer/Wyeth   CD22         Calicheamicin   \n",
       "4                         AstraZeneca   CD22  Pseudomonas exotoxin   \n",
       "\n",
       "     Antibody                                   Indication  \\\n",
       "0   Gemtuzumab                      Acute myeloid leukemia   \n",
       "1  Brentuximab       Hodgkin lymphoma, large cell lymphoma   \n",
       "2  Trastuzumab                         HER2+ breast cancer   \n",
       "3   Inotuzumab                Acute lymphoblastic leukemia   \n",
       "4  Moxetumomab  Relapsed or refractory hairy cell leukemia   \n",
       "\n",
       "                                      Payload SMILES  \\\n",
       "0  CCN(C(C)=O)[C@H]1CO[C@@H](O[C@@H]2[C@@H](O)[C@...   \n",
       "1  CC[C@H](C)[C@@H]([C@@H](CC(=O)N1CCC[C@H]1[C@@H...   \n",
       "2  C[C@@H]1[C@@H]2C[C@]([C@@H](/C=C/C=C(/CC3=CC(=...   \n",
       "3  CCN(C(C)=O)[C@H]1CO[C@@H](O[C@@H]2[C@@H](O)[C@...   \n",
       "4                                                NaN   \n",
       "\n",
       "                        Launch year  \n",
       "0  2000.05\\r\\nRelaunched in 2017.09  \n",
       "1                           2011.08  \n",
       "2                           2013.02  \n",
       "3                           2017.08  \n",
       "4                           2018.09  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some Appoved ADCs\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5486a4d1",
   "metadata": {},
   "source": [
    "Lets move now to visualise some approved ADCs warheads to get better idea of their molecular connectivity, functional groups and stereochemistry, as they heavily influence their mechanism of action, binding affinity, and toxicity profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d08a6050",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = df.dropna(subset=[\"Payload SMILES\"]).drop_duplicates(subset=[\"Payload\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5c00955",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets get valid Payload SMILES only\n",
    "def is_valid_smiles(smiles):\n",
    "    try:\n",
    "        mol = Chem.MolFromSmiles(smiles)\n",
    "        return mol is not None\n",
    "    except:\n",
    "        return False\n",
    "df_valid = df_clean[df_clean[\"Payload SMILES\"].apply(is_valid_smiles)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "931e83cc",
   "metadata": {},
   "source": [
    "We'll begin by examining a diverse set of 8 unique ADC warheads that have been used in FDA-approved therapies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5623b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grid = df_valid.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "050184ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting each SMILES to RDKit Mol object\n",
    "\n",
    "mols = [Chem.MolFromSmiles(smiles) for smiles in df_grid[\"Payload SMILES\"]]\n",
    "\n",
    "img = Draw.MolsToGridImage(\n",
    "    mols,\n",
    "    molsPerRow=4,\n",
    "    subImgSize=(450, 350),\n",
    "    legends=df_grid[\"Payload\"].tolist()\n",
    ")\n",
    "\n",
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d0d388",
   "metadata": {},
   "source": [
    "Similarly, lets visualise the 3D conformations of few ADC warheads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6b13d7fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "import py3Dmol\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "# Generating 3D conformers\n",
    "\n",
    "def generate_3d_mol(smiles):\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "    mol = Chem.AddHs(mol)\n",
    "    if AllChem.EmbedMolecule(mol, AllChem.ETKDG()) == 0:\n",
    "        AllChem.MMFFOptimizeMolecule(mol)\n",
    "        return mol\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2bd3ff9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating 3D molecules\n",
    "\n",
    "mols_3d = [generate_3d_mol(smiles) for smiles in df_grid[\"Payload SMILES\"][:5]]\n",
    "mols_3d = [mol for mol in mols_3d if mol is not None]\n",
    "payloads = df_grid.iloc[:len(mols_3d)][\"Payload\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9d428b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create HTML block for each molecule\n",
    "\n",
    "def mol_to_html(mol, title, width=300, height=300):\n",
    "    mol_block = Chem.MolToMolBlock(mol)\n",
    "    viewer = py3Dmol.view(width=width, height=height)\n",
    "    viewer.addModel(mol_block, \"mol\")\n",
    "    viewer.setStyle({\"stick\": {}})\n",
    "    viewer.setBackgroundColor(\"white\")\n",
    "    viewer.zoomTo()\n",
    "    html = viewer._make_html()\n",
    "    html += f\"<div style='text-align:center; font-size:14px; font-weight:bold'>{title}</div>\"\n",
    "    return html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dd109cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><td style='padding:10px; vertical-align:top; border:none'><div id=\"3dmolviewer_1751542650597935\"  style=\"position: relative; width: 300px; height: 300px;\">\n",
       "        <p id=\"3dmolwarning_1751542650597935\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n",
       "        </div>\n",
       "<script>\n",
       "\n",
       "var loadScriptAsync = function(uri){\n",
       "  return new Promise((resolve, reject) => {\n",
       "    //this is to ignore the existence of requirejs amd\n",
       "    var savedexports, savedmodule;\n",
       "    if (typeof exports !== 'undefined') savedexports = exports;\n",
       "    else exports = {}\n",
       "    if (typeof module !== 'undefined') savedmodule = module;\n",
       "    else module = {}\n",
       "\n",
       "    var tag = document.createElement('script');\n",
       "    tag.src = uri;\n",
       "    tag.async = true;\n",
       "    tag.onload = () => {\n",
       "        exports = savedexports;\n",
       "        module = savedmodule;\n",
       "        resolve();\n",
       "    };\n",
       "  var firstScriptTag = document.getElementsByTagName('script')[0];\n",
       "  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
       "});\n",
       "};\n",
       "\n",
       "if(typeof $3Dmolpromise === 'undefined') {\n",
       "$3Dmolpromise = null;\n",
       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.4.2/3Dmol-min.js');\n",
       "}\n",
       "\n",
       "var viewer_1751542650597935 = null;\n",
       "var warn = document.getElementById(\"3dmolwarning_1751542650597935\");\n",
       "if(warn) {\n",
       "    warn.parentNode.removeChild(warn);\n",
       "}\n",
       "$3Dmolpromise.then(function() {\n",
       "viewer_1751542650597935 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1751542650597935\"),{backgroundColor:\"white\"});\n",
       "viewer_1751542650597935.zoomTo();\n",
       "\tviewer_1751542650597935.addModel(\"\\n     RDKit          3D\\n\\n118119  0  0  0  0  0  0  0  0999 V2000\\n   -2.1358   -2.1302   -5.9204 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.2982   -1.6561   -4.4793 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.6785   -2.7847   -3.4995 C   0  0  2  0  0  0  0  0  0  0  0  0\\n   -4.0749   -3.3475   -3.8145 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.5592   -2.3832   -1.9838 C   0  0  1  0  0  0  0  0  0  0  0  0\\n   -1.0757   -2.1618   -1.5228 C   0  0  1  0  0  0  0  0  0  0  0  0\\n   -0.9857   -1.8493   -0.0165 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.3960   -1.3556    0.3821 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.2187   -0.9605   -0.4399 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.6850   -1.3577    1.7321 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.2898   -1.3852    2.8110 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.5229   -1.2619    4.0893 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.7967   -0.5552    3.6410 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.0456   -1.0672    2.2153 C   0  0  1  0  0  0  0  0  0  0  0  0\\n    2.9701   -2.3267    2.1243 C   0  0  1  0  0  0  0  0  0  0  0  0\\n    4.4575   -1.9586    2.3553 C   0  0  1  0  0  0  0  0  0  0  0  0\\n    5.3177   -3.1742    2.7091 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.0422   -1.3220    1.1020 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.5540   -1.4507   -0.0123 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.1930   -0.5939    1.3189 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.9276    0.0399    0.2206 C   0  0  2  0  0  0  0  0  0  0  0  0\\n    8.2624   -0.6929    0.0320 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.1472    1.5634    0.4632 C   0  0  2  0  0  0  0  0  0  0  0  0\\n    5.9086    2.4261    0.6562 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.0462    3.6666    1.3084 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.9481    4.5147    1.4772 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.6975    4.1410    0.9826 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.5473    2.9241    0.3252 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.6462    2.0757    0.1498 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.9057    2.1089   -0.6218 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.5864   -3.2971    3.1214 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.9071   -4.4199    2.5699 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.3956   -3.4240   -1.7308 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.7638   -3.3056   -2.5490 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.5386   -1.3324   -1.5852 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7709   -1.7884   -0.9459 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2833    0.0195   -1.8116 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.1804    0.4179   -2.2041 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.3816    1.0610   -1.4884 C   0  0  2  0  0  0  0  0  0  0  0  0\\n   -5.0474    1.6987   -2.7312 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.9191    0.6958   -3.4884 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.0608    2.3724   -3.6878 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.7222    2.0803   -0.6678 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.3766    2.7367    0.3475 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.5550    2.5152    0.6263 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.5167    3.7677    1.1235 C   0  0  2  0  0  0  0  0  0  0  0  0\\n   -2.5839    3.0943    2.1655 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2904    2.0774    3.0667 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.3508    2.4460    1.5212 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.4245    4.7210    1.8201 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.9009    5.7592    0.8982 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.7966   -1.2972   -6.5445 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.3956   -2.9356   -5.9863 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.0826   -2.4860   -6.3368 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.3493   -1.2000   -4.1816 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.0546   -0.8660   -4.4572 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.9734   -3.6121   -3.6673 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.0830   -3.8356   -4.7933 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.8363   -2.5599   -3.8289 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.3688   -4.1011   -3.0782 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.8581   -3.2887   -1.4363 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.5949   -1.3700   -2.1030 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.6940   -1.0670    0.2740 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.2155   -2.7599    0.5482 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.8748   -2.3142    2.7811 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.9607   -0.5254    2.6766 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.7482   -2.2567    4.4896 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.0075   -0.7168    4.8757 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.6282   -0.7118    4.3319 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.6098    0.5237    3.5969 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.4702   -0.2689    1.5978 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8573   -2.7784    1.1331 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.5333   -1.2406    3.1821 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.2593   -3.9440    1.9284 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.0002   -3.6253    3.6545 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.3740   -2.9006    2.8219 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.5908   -0.5588    2.2451 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.3686   -0.0877   -0.7177 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    8.0904   -1.7674   -0.1103 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    8.9054   -0.5724    0.9158 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    8.8032   -0.3165   -0.8459 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.7663    1.6675    1.3671 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.0179    3.9742    1.6901 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.0754    5.4654    1.9848 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8451    4.8016    1.1149 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.5746    2.6329   -0.0660 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.4789    1.1456   -0.3898 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.3420    2.7531   -1.0904 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.9863   -4.1136    2.0701 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.6480   -5.1053    3.3823 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.5530   -4.9512    1.8672 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.5634   -2.7933   -2.0168 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.1084   -4.3121   -2.7890 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.5376   -2.7882   -3.4834 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7750   -2.8736   -0.8038 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.6262   -1.5152   -1.5681 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.8596   -1.3111    0.0358 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.1661    0.6176   -0.8703 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.7178    2.4858   -2.3636 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.4721    1.1961   -4.2890 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.3197   -0.0984   -3.9400 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.6513    0.2262   -2.8220 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.6002    2.9093   -4.4737 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.4129    1.6389   -4.1824 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.4243    3.0990   -3.1717 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.7693    2.3249   -0.9211 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.8941    4.3218    0.4085 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.1990    3.8892    2.8189 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.1897    2.5021    3.5217 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.5713    1.1739    2.5103 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.6227    1.7680    3.8784 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.6374    2.1319    2.2932 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.8327    3.1536    0.8632 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.6079    1.5542    0.9399 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.2443    4.1687    2.1235 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.3547    5.3335   -0.0121 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.0777    6.4277    0.6207 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.6608    6.3586    1.4122 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  3  1  0\\n  3  4  1  0\\n  3  5  1  0\\n  5  6  1  0\\n  6  7  1  0\\n  7  8  1  0\\n  8  9  2  0\\n  8 10  1  0\\n 10 11  1  0\\n 11 12  1  0\\n 12 13  1  0\\n 13 14  1  0\\n 14 15  1  0\\n 15 16  1  0\\n 16 17  1  0\\n 16 18  1  0\\n 18 19  2  0\\n 18 20  1  0\\n 20 21  1  0\\n 21 22  1  0\\n 21 23  1  0\\n 23 24  1  0\\n 24 25  2  0\\n 25 26  1  0\\n 26 27  2  0\\n 27 28  1  0\\n 28 29  2  0\\n 23 30  1  0\\n 15 31  1  0\\n 31 32  1  0\\n  6 33  1  0\\n 33 34  1  0\\n  5 35  1  0\\n 35 36  1  0\\n 35 37  1  0\\n 37 38  2  0\\n 37 39  1  0\\n 39 40  1  0\\n 40 41  1  0\\n 40 42  1  0\\n 39 43  1  0\\n 43 44  1  0\\n 44 45  2  0\\n 44 46  1  0\\n 46 47  1  0\\n 47 48  1  0\\n 47 49  1  0\\n 46 50  1  0\\n 50 51  1  0\\n 14 10  1  0\\n 29 24  1  0\\n  1 52  1  0\\n  1 53  1  0\\n  1 54  1  0\\n  2 55  1  0\\n  2 56  1  0\\n  3 57  1  1\\n  4 58  1  0\\n  4 59  1  0\\n  4 60  1  0\\n  5 61  1  1\\n  6 62  1  6\\n  7 63  1  0\\n  7 64  1  0\\n 11 65  1  0\\n 11 66  1  0\\n 12 67  1  0\\n 12 68  1  0\\n 13 69  1  0\\n 13 70  1  0\\n 14 71  1  6\\n 15 72  1  6\\n 16 73  1  1\\n 17 74  1  0\\n 17 75  1  0\\n 17 76  1  0\\n 20 77  1  0\\n 21 78  1  6\\n 22 79  1  0\\n 22 80  1  0\\n 22 81  1  0\\n 23 82  1  1\\n 25 83  1  0\\n 26 84  1  0\\n 27 85  1  0\\n 28 86  1  0\\n 29 87  1  0\\n 30 88  1  0\\n 32 89  1  0\\n 32 90  1  0\\n 32 91  1  0\\n 34 92  1  0\\n 34 93  1  0\\n 34 94  1  0\\n 36 95  1  0\\n 36 96  1  0\\n 36 97  1  0\\n 39 98  1  1\\n 40 99  1  0\\n 41100  1  0\\n 41101  1  0\\n 41102  1  0\\n 42103  1  0\\n 42104  1  0\\n 42105  1  0\\n 43106  1  0\\n 46107  1  6\\n 47108  1  0\\n 48109  1  0\\n 48110  1  0\\n 48111  1  0\\n 49112  1  0\\n 49113  1  0\\n 49114  1  0\\n 50115  1  0\\n 51116  1  0\\n 51117  1  0\\n 51118  1  0\\nM  END\\n\",\"mol\");\n",
       "\tviewer_1751542650597935.setStyle({\"stick\": {}});\n",
       "\tviewer_1751542650597935.setBackgroundColor(\"white\");\n",
       "\tviewer_1751542650597935.zoomTo();\n",
       "viewer_1751542650597935.render();\n",
       "});\n",
       "</script><div style='text-align:center; font-size:14px; font-weight:bold'>Calicheamicin</div></td><td style='padding:10px; vertical-align:top; border:none'><div id=\"3dmolviewer_17515426505993648\"  style=\"position: relative; width: 300px; height: 300px;\">\n",
       "        <p id=\"3dmolwarning_17515426505993648\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n",
       "        </div>\n",
       "<script>\n",
       "\n",
       "var loadScriptAsync = function(uri){\n",
       "  return new Promise((resolve, reject) => {\n",
       "    //this is to ignore the existence of requirejs amd\n",
       "    var savedexports, savedmodule;\n",
       "    if (typeof exports !== 'undefined') savedexports = exports;\n",
       "    else exports = {}\n",
       "    if (typeof module !== 'undefined') savedmodule = module;\n",
       "    else module = {}\n",
       "\n",
       "    var tag = document.createElement('script');\n",
       "    tag.src = uri;\n",
       "    tag.async = true;\n",
       "    tag.onload = () => {\n",
       "        exports = savedexports;\n",
       "        module = savedmodule;\n",
       "        resolve();\n",
       "    };\n",
       "  var firstScriptTag = document.getElementsByTagName('script')[0];\n",
       "  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
       "});\n",
       "};\n",
       "\n",
       "if(typeof $3Dmolpromise === 'undefined') {\n",
       "$3Dmolpromise = null;\n",
       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.4.2/3Dmol-min.js');\n",
       "}\n",
       "\n",
       "var viewer_17515426505993648 = null;\n",
       "var warn = document.getElementById(\"3dmolwarning_17515426505993648\");\n",
       "if(warn) {\n",
       "    warn.parentNode.removeChild(warn);\n",
       "}\n",
       "$3Dmolpromise.then(function() {\n",
       "viewer_17515426505993648 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17515426505993648\"),{backgroundColor:\"white\"});\n",
       "viewer_17515426505993648.zoomTo();\n",
       "\tviewer_17515426505993648.addModel(\"\\n     RDKit          3D\\n\\n 98101  0  0  0  0  0  0  0  0999 V2000\\n    0.6772   -3.0467    2.3955 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.4261   -2.2114    1.7396 C   0  0  2  0  0  0  0  0  0  0  0  0\\n   -1.1461   -2.9702    0.5972 C   0  0  2  0  0  0  0  0  0  0  0  0\\n   -2.4499   -2.2974    0.1811 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.1917   -3.1610   -0.8400 C   0  0  2  0  0  0  0  0  0  0  0  0\\n   -4.7113   -3.3760   -0.4714 C   0  0  1  0  0  0  0  0  0  0  0  0\\n   -5.6897   -2.2430   -0.2794 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.6344   -0.9053   -0.3420 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.5307   -0.0398   -0.6576 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.3415    0.5943   -1.8332 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2219    1.5951   -2.1126 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.5229    2.2941   -0.9579 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.1567    2.6197   -1.0696 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.4759    3.3119   -0.0524 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.1910    3.6544    1.1186 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.5739    3.4054    1.2153 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2284    2.7590    0.1619 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.1997    3.8114    2.3671 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.6226    3.8156    2.3777 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.3692    4.3286    2.5000 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n    0.8567    3.7816   -0.2919 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.9085    3.0669   -0.8599 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8844    3.6454   -1.3539 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.8955    1.5524   -0.8628 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.8693    0.9305    0.5388 C   0  0  1  0  0  0  0  0  0  0  0  0\\n    1.2865   -0.4818    0.4721 C   0  0  1  0  0  0  0  0  0  0  0  0\\n    0.0832   -0.8312    1.3706 C   0  0  1  0  0  0  0  0  0  0  0  0\\n   -0.0599   -0.4832   -0.0104 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.1474   -1.5334   -0.1646 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.2155    0.8008    1.0414 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.7117    1.8432    1.7618 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.0512    2.8154    2.1103 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.1689    1.5946    2.2003 C   0  0  2  0  0  0  0  0  0  0  0  0\\n    5.9779    2.8912    2.1807 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.8503    0.4754    1.5039 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.1219   -0.7151    2.2978 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.1301    0.6100    0.1478 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.7675    1.6031   -0.4826 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.8795   -0.5215   -0.5367 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.2230   -0.2116   -1.9920 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    8.5628    1.0168   -2.1355 S   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.0790    5.2221   -0.1646 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.2315    0.3719   -3.0351 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.2726   -4.1612   -1.5606 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.9229   -5.3454   -1.1065 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.5484   -4.4508   -0.9923 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.0357   -5.1036    0.0873 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.0753   -6.3219    0.1910 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.4900   -4.2987    1.0380 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.0770   -2.5840   -2.1349 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.2388   -2.4533    3.1251 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.2516   -3.9050    2.9266 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.3870   -3.4350    1.6607 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.1723   -2.0539    2.5333 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.4709   -3.0651   -0.2603 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.0698   -2.1736    1.0773 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.2491   -1.3078   -0.2275 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7331   -3.9675    0.4573 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.6777   -2.6276   -0.0199 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.5562   -0.3589   -0.1315 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.8653    0.1517    0.1817 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.4736    1.0773   -2.7287 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.6300    2.4017   -2.7362 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.6468    2.3877   -2.0038 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.3005    2.5879    0.2003 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.0252    4.4097    1.5507 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.9474    4.2846    3.3108 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.0160    2.7944    2.3633 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8077    1.2318   -1.3840 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.0717    1.2103   -1.4914 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.2648    1.5185    1.2373 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.2133   -0.0947    2.1116 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.6654   -1.1398   -1.0441 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.5780   -2.4050   -0.4977 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.9075   -1.8757    0.5432 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.0878    1.2867    3.2520 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.9567    3.3863    1.2058 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.0243    2.7050    2.4491 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.5700    3.6047    2.9064 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.7881   -0.4418    3.1210 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.1769   -1.0896    2.7053 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.5876   -1.5159    1.7252 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.2207   -1.3993   -0.5244 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.7907   -0.7566    0.0255 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.3502    0.1516   -2.5447 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.5719   -1.1272   -2.4827 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.8424    2.0540   -1.6772 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.4517    5.6216   -1.1152 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.1654    5.7706    0.0837 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.8426    5.3933    0.6011 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.0121   -0.3740   -2.8667 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.6323    0.0298   -3.8869 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.7270    1.3091   -3.3137 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.3544   -5.8516   -1.9727 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.2011   -6.0176   -0.6334 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.7297   -5.1043   -0.4073 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.9726   -5.0669   -1.6691 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.8740   -2.8770   -2.6162 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  3  1  0\\n  3  4  1  0\\n  4  5  1  0\\n  5  6  1  0\\n  6  7  1  0\\n  7  8  2  0\\n  8  9  1  0\\n  9 10  2  0\\n 10 11  1  0\\n 11 12  1  0\\n 12 13  2  0\\n 13 14  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 16 17  2  0\\n 16 18  1  0\\n 18 19  1  0\\n 15 20  1  0\\n 14 21  1  0\\n 21 22  1  0\\n 22 23  2  0\\n 22 24  1  0\\n 24 25  1  0\\n 25 26  1  0\\n 26 27  1  0\\n 27 28  1  0\\n 26 29  1  6\\n 25 30  1  0\\n 30 31  1  0\\n 31 32  2  0\\n 31 33  1  0\\n 33 34  1  0\\n 33 35  1  0\\n 35 36  1  0\\n 35 37  1  0\\n 37 38  2  0\\n 37 39  1  0\\n 39 40  1  0\\n 40 41  1  0\\n 21 42  1  0\\n 10 43  1  0\\n  6 44  1  0\\n 44 45  1  0\\n  5 46  1  0\\n 46 47  1  0\\n 47 48  2  0\\n 47 49  1  0\\n  5 50  1  6\\n 27  2  1  0\\n 49  3  1  0\\n 17 12  1  0\\n 28 26  1  0\\n  1 51  1  0\\n  1 52  1  0\\n  1 53  1  0\\n  2 54  1  1\\n  3 55  1  6\\n  4 56  1  0\\n  4 57  1  0\\n  6 58  1  1\\n  7 59  1  0\\n  8 60  1  0\\n  9 61  1  0\\n 11 62  1  0\\n 11 63  1  0\\n 13 64  1  0\\n 17 65  1  0\\n 19 66  1  0\\n 19 67  1  0\\n 19 68  1  0\\n 24 69  1  0\\n 24 70  1  0\\n 25 71  1  1\\n 27 72  1  1\\n 29 73  1  0\\n 29 74  1  0\\n 29 75  1  0\\n 33 76  1  1\\n 34 77  1  0\\n 34 78  1  0\\n 34 79  1  0\\n 36 80  1  0\\n 36 81  1  0\\n 36 82  1  0\\n 39 83  1  0\\n 39 84  1  0\\n 40 85  1  0\\n 40 86  1  0\\n 41 87  1  0\\n 42 88  1  0\\n 42 89  1  0\\n 42 90  1  0\\n 43 91  1  0\\n 43 92  1  0\\n 43 93  1  0\\n 45 94  1  0\\n 45 95  1  0\\n 45 96  1  0\\n 46 97  1  0\\n 50 98  1  0\\nM  END\\n\",\"mol\");\n",
       "\tviewer_17515426505993648.setStyle({\"stick\": {}});\n",
       "\tviewer_17515426505993648.setBackgroundColor(\"white\");\n",
       "\tviewer_17515426505993648.zoomTo();\n",
       "viewer_17515426505993648.render();\n",
       "});\n",
       "</script><div style='text-align:center; font-size:14px; font-weight:bold'>MMAE</div></td><td style='padding:10px; vertical-align:top; border:none'><div id=\"3dmolviewer_1751542650600039\"  style=\"position: relative; width: 300px; height: 300px;\">\n",
       "        <p id=\"3dmolwarning_1751542650600039\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n",
       "        </div>\n",
       "<script>\n",
       "\n",
       "var loadScriptAsync = function(uri){\n",
       "  return new Promise((resolve, reject) => {\n",
       "    //this is to ignore the existence of requirejs amd\n",
       "    var savedexports, savedmodule;\n",
       "    if (typeof exports !== 'undefined') savedexports = exports;\n",
       "    else exports = {}\n",
       "    if (typeof module !== 'undefined') savedmodule = module;\n",
       "    else module = {}\n",
       "\n",
       "    var tag = document.createElement('script');\n",
       "    tag.src = uri;\n",
       "    tag.async = true;\n",
       "    tag.onload = () => {\n",
       "        exports = savedexports;\n",
       "        module = savedmodule;\n",
       "        resolve();\n",
       "    };\n",
       "  var firstScriptTag = document.getElementsByTagName('script')[0];\n",
       "  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
       "});\n",
       "};\n",
       "\n",
       "if(typeof $3Dmolpromise === 'undefined') {\n",
       "$3Dmolpromise = null;\n",
       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.4.2/3Dmol-min.js');\n",
       "}\n",
       "\n",
       "var viewer_1751542650600039 = null;\n",
       "var warn = document.getElementById(\"3dmolwarning_1751542650600039\");\n",
       "if(warn) {\n",
       "    warn.parentNode.removeChild(warn);\n",
       "}\n",
       "$3Dmolpromise.then(function() {\n",
       "viewer_1751542650600039 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1751542650600039\"),{backgroundColor:\"white\"});\n",
       "viewer_1751542650600039.zoomTo();\n",
       "\tviewer_1751542650600039.addModel(\"\\n     RDKit          3D\\n\\n 60 65  0  0  0  0  0  0  0  0999 V2000\\n    7.1356    1.1867    0.2115 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.7607    0.5725    0.4589 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.1721   -0.1846   -0.7513 C   0  0  2  0  0  0  0  0  0  0  0  0\\n    3.7549   -0.6163   -0.4636 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.5142   -1.8485    0.0239 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.5904   -2.8454    0.2730 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.8428   -2.5521   -0.3829 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.0505   -1.4138   -1.0791 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.9547   -1.3371   -1.9123 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.1185   -2.2765    0.3611 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.8905   -3.3836    0.8363 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.1523   -1.3364    0.1113 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.2791   -1.5340    0.3500 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.8726   -0.2175   -0.0668 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.1877    0.2308   -0.1044 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.3543   -0.6360    0.3252 C   0  0  2  0  0  0  0  0  0  0  0  0\\n   -4.5913    0.2125    0.6424 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.9170    1.1687   -0.4941 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.7280    2.0378   -0.8408 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.4167    1.5212   -0.6544 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.2885    2.3027   -1.0498 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.4970    3.5618   -1.5725 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.7806    4.0595   -1.7503 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.9106    3.3126   -1.4157 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.2743    3.8958   -1.6596 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.9201    5.2862   -2.2739 F   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.0205    1.8751   -0.9607 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.1397    0.6322   -0.4988 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.4091   -0.0750   -0.3836 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.6532    0.3156   -0.6733 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.0315   -1.4327    1.5018 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.6672   -2.6330    1.7383 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.4267   -3.1710    0.9366 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.3294   -3.2765    3.0840 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.2575   -4.2891    3.4339 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.1654    0.6873   -1.8894 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.8785    0.4239   -0.0374 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.4799    1.6999    1.1150 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.1054    1.9219   -0.5975 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.8296   -0.0954    1.3271 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.0892    1.3944    0.7402 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.2890   -3.8336   -0.0899 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.7932   -2.9288    1.3459 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.6332   -2.3663   -0.2659 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.4181   -1.7464    1.4139 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.5867   -1.3130   -0.5071 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.4222    0.7895    1.5633 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.4569   -0.4325    0.8385 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.2174    0.6099   -1.3887 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.7690    1.7807   -0.1788 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.6492    4.1721   -1.8690 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.9032    3.1861   -2.2065 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.7514    4.1495   -0.7078 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.2263    4.8074   -2.2639 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8516    1.3075   -1.0707 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.6317   -0.9449    2.2946 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.3310   -3.7201    3.0191 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.3377   -2.5180    3.8730 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.5542   -4.6875    2.5913 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.0179    0.5436   -2.3492 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  3  1  0\\n  3  4  1  0\\n  4  5  2  0\\n  5  6  1  0\\n  6  7  1  0\\n  7  8  1  0\\n  8  9  2  0\\n  5 10  1  0\\n 10 11  2  0\\n 10 12  1  0\\n 12 13  1  0\\n 13 14  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 16 17  1  0\\n 17 18  1  0\\n 18 19  1  0\\n 19 20  1  0\\n 20 21  2  0\\n 21 22  1  0\\n 22 23  2  0\\n 23 24  1  0\\n 24 25  1  0\\n 23 26  1  0\\n 21 27  1  0\\n 27 28  2  0\\n 28 29  1  0\\n 29 30  2  0\\n 16 31  1  0\\n 31 32  1  0\\n 32 33  2  0\\n 32 34  1  0\\n 34 35  1  0\\n  3 36  1  6\\n  8  3  1  0\\n 30  4  1  0\\n 29 12  1  0\\n 28 14  1  0\\n 20 15  1  0\\n 24 19  2  0\\n  1 37  1  0\\n  1 38  1  0\\n  1 39  1  0\\n  2 40  1  0\\n  2 41  1  0\\n  6 42  1  0\\n  6 43  1  0\\n 13 44  1  0\\n 13 45  1  0\\n 16 46  1  6\\n 17 47  1  0\\n 17 48  1  0\\n 18 49  1  0\\n 18 50  1  0\\n 22 51  1  0\\n 25 52  1  0\\n 25 53  1  0\\n 25 54  1  0\\n 30 55  1  0\\n 31 56  1  0\\n 34 57  1  0\\n 34 58  1  0\\n 35 59  1  0\\n 36 60  1  0\\nM  END\\n\",\"mol\");\n",
       "\tviewer_1751542650600039.setStyle({\"stick\": {}});\n",
       "\tviewer_1751542650600039.setBackgroundColor(\"white\");\n",
       "\tviewer_1751542650600039.zoomTo();\n",
       "viewer_1751542650600039.render();\n",
       "});\n",
       "</script><div style='text-align:center; font-size:14px; font-weight:bold'>DM1</div></td><td style='padding:10px; vertical-align:top; border:none'><div id=\"3dmolviewer_1751542650600621\"  style=\"position: relative; width: 300px; height: 300px;\">\n",
       "        <p id=\"3dmolwarning_1751542650600621\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n",
       "        </div>\n",
       "<script>\n",
       "\n",
       "var loadScriptAsync = function(uri){\n",
       "  return new Promise((resolve, reject) => {\n",
       "    //this is to ignore the existence of requirejs amd\n",
       "    var savedexports, savedmodule;\n",
       "    if (typeof exports !== 'undefined') savedexports = exports;\n",
       "    else exports = {}\n",
       "    if (typeof module !== 'undefined') savedmodule = module;\n",
       "    else module = {}\n",
       "\n",
       "    var tag = document.createElement('script');\n",
       "    tag.src = uri;\n",
       "    tag.async = true;\n",
       "    tag.onload = () => {\n",
       "        exports = savedexports;\n",
       "        module = savedmodule;\n",
       "        resolve();\n",
       "    };\n",
       "  var firstScriptTag = document.getElementsByTagName('script')[0];\n",
       "  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
       "});\n",
       "};\n",
       "\n",
       "if(typeof $3Dmolpromise === 'undefined') {\n",
       "$3Dmolpromise = null;\n",
       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.4.2/3Dmol-min.js');\n",
       "}\n",
       "\n",
       "var viewer_1751542650600621 = null;\n",
       "var warn = document.getElementById(\"3dmolwarning_1751542650600621\");\n",
       "if(warn) {\n",
       "    warn.parentNode.removeChild(warn);\n",
       "}\n",
       "$3Dmolpromise.then(function() {\n",
       "viewer_1751542650600621 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1751542650600621\"),{backgroundColor:\"white\"});\n",
       "viewer_1751542650600621.zoomTo();\n",
       "\tviewer_1751542650600621.addModel(\"\\n     RDKit          3D\\n\\n 49 53  0  0  0  0  0  0  0  0999 V2000\\n   -4.3324    2.5504   -0.9667 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.5783    2.4293    0.3495 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.8565    1.1087    0.4699 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.5257    1.0130    0.0873 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.8780   -0.2127    0.1885 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.4142   -1.3531    0.6324 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.7036   -1.2825    0.9972 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2981   -2.4511    1.4532 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.6348   -2.4963    1.8533 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.3924   -1.3402    1.7928 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.7023   -1.3261    2.1674 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.8339   -0.1486    1.3431 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.4818   -0.0841    0.9369 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.4932   -0.0376   -0.2747 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.4962   -0.9141   -0.3721 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.7867   -0.4613   -0.8777 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.9542    0.8208   -1.2498 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.8252    1.8015   -1.1387 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.9722    2.9752   -1.4623 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.6569    1.2811   -0.6464 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.5791    2.0445   -0.4559 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.2376    1.3651   -1.7649 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.2564    0.3798   -2.0350 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.0756   -0.9457   -1.8669 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.8449   -1.7553   -2.3891 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.9308   -1.4330   -0.9525 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.4356   -2.6532   -1.5402 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.5109   -1.8079    0.4291 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.0264   -0.6379    1.2576 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.0936    1.7701   -1.0684 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.8342    3.5214   -1.0260 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.6528    2.4680   -1.8216 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.8616    3.2557    0.4288 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.2605    2.5714    1.1949 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.7000   -3.3592    1.4963 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.0539   -3.4348    2.2012 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.9575   -2.2196    2.4517 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.4747    0.7279    1.3118 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.3598   -1.9553   -0.0924 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.9052    2.4435   -1.4212 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.3898    2.8540    0.2557 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.0741    1.8984   -2.7076 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.6622    2.0752   -1.0473 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.2215   -3.1350   -1.8690 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.7549   -2.3496    1.0130 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.3317   -2.5254    0.2942 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.4428   -1.0061    2.2010 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.2257    0.0656    1.5042 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.8194   -0.0950    0.7352 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  3  1  0\\n  3  4  2  0\\n  4  5  1  0\\n  5  6  2  0\\n  6  7  1  0\\n  7  8  2  0\\n  8  9  1  0\\n  9 10  2  0\\n 10 11  1  0\\n 10 12  1  0\\n 12 13  2  0\\n  5 14  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 16 17  2  0\\n 17 18  1  0\\n 18 19  2  0\\n 18 20  1  0\\n 20 21  1  0\\n 17 22  1  0\\n 22 23  1  0\\n 23 24  1  0\\n 24 25  2  0\\n 24 26  1  0\\n 26 27  1  0\\n 26 28  1  0\\n 28 29  1  0\\n 13  3  1  0\\n 20 14  1  0\\n 21  4  1  0\\n 13  7  1  0\\n 26 16  1  0\\n  1 30  1  0\\n  1 31  1  0\\n  1 32  1  0\\n  2 33  1  0\\n  2 34  1  0\\n  8 35  1  0\\n  9 36  1  0\\n 11 37  1  0\\n 12 38  1  0\\n 15 39  1  0\\n 21 40  1  0\\n 21 41  1  0\\n 22 42  1  0\\n 22 43  1  0\\n 27 44  1  0\\n 28 45  1  0\\n 28 46  1  0\\n 29 47  1  0\\n 29 48  1  0\\n 29 49  1  0\\nM  END\\n\",\"mol\");\n",
       "\tviewer_1751542650600621.setStyle({\"stick\": {}});\n",
       "\tviewer_1751542650600621.setBackgroundColor(\"white\");\n",
       "\tviewer_1751542650600621.zoomTo();\n",
       "viewer_1751542650600621.render();\n",
       "});\n",
       "</script><div style='text-align:center; font-size:14px; font-weight:bold'>Dxd</div></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate viewer blocks\n",
    "html_blocks = [mol_to_html(mol, title) for mol, title in zip(mols_3d, payloads)]\n",
    "\n",
    "grid_html = \"<table><tr>\"\n",
    "for cell in html_blocks:\n",
    "    grid_html += f\"<td style='padding:10px; vertical-align:top; border:none'>{cell}</td>\"\n",
    "grid_html += \"</tr></table>\"\n",
    "\n",
    "display(HTML(grid_html))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4402f92c",
   "metadata": {},
   "source": [
    "So far, we have gained a foundational understanding of Antibody-Drug Conjugates (ADCs) and their key properties by visualizing the 2D and 3D structures of their payloads.\n",
    "\n",
    "Now, it’s time to explore and predict ADC payload activity using a comprehensive dataset from ChEMBL [[10]](#10), which contains detailed information on molecules targeting DNA topoisomerase I, an enzyme often inhibited by ADC payloads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d1e42b82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original rows: 7637\n"
     ]
    }
   ],
   "source": [
    "# loading data\n",
    "\n",
    "url = \"https://tutorial-fast-loader.s3.ap-south-1.amazonaws.com/ADC/ChEMBL.csv\"\n",
    "adc_db = pd.read_csv(url, sep = \";\")\n",
    "print(\"Original rows:\", len(adc_db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "38f20fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Molecule ChEMBL ID</th>\n",
       "      <th>Molecule Name</th>\n",
       "      <th>Molecule Max Phase</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>#RO5 Violations</th>\n",
       "      <th>AlogP</th>\n",
       "      <th>Compound Key</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>Standard Type</th>\n",
       "      <th>Standard Relation</th>\n",
       "      <th>...</th>\n",
       "      <th>Document ChEMBL ID</th>\n",
       "      <th>Source ID</th>\n",
       "      <th>Source Description</th>\n",
       "      <th>Document Journal</th>\n",
       "      <th>Document Year</th>\n",
       "      <th>Cell ChEMBL ID</th>\n",
       "      <th>Properties</th>\n",
       "      <th>Action Type</th>\n",
       "      <th>Standard Text Value</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL540569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>7d</td>\n",
       "      <td>CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2cc3c(NC(=O)CN)c...</td>\n",
       "      <td>IC50</td>\n",
       "      <td>'='</td>\n",
       "      <td>...</td>\n",
       "      <td>CHEMBL1126797</td>\n",
       "      <td>1</td>\n",
       "      <td>Scientific Literature</td>\n",
       "      <td>J Med Chem</td>\n",
       "      <td>1993</td>\n",
       "      <td>CHEMBL3307654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL3953305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>646.88</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8</td>\n",
       "      <td>C#CCOCCCCCCCCCCCCOc1ccc(-c2nc3ccc(-c4nc5ccc(N6...</td>\n",
       "      <td>IC50</td>\n",
       "      <td>'&gt;'</td>\n",
       "      <td>...</td>\n",
       "      <td>CHEMBL4014329</td>\n",
       "      <td>1</td>\n",
       "      <td>Scientific Literature</td>\n",
       "      <td>J Med Chem</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL88605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>558.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.53</td>\n",
       "      <td>5an</td>\n",
       "      <td>CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2cc3cc(OC(=O)N4C...</td>\n",
       "      <td>IC50</td>\n",
       "      <td>'='</td>\n",
       "      <td>...</td>\n",
       "      <td>CHEMBL1126797</td>\n",
       "      <td>1</td>\n",
       "      <td>Scientific Literature</td>\n",
       "      <td>J Med Chem</td>\n",
       "      <td>1993</td>\n",
       "      <td>CHEMBL3307654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL80138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318.37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>table 2</td>\n",
       "      <td>CCCCCC(O)c1cc(OC)c2c(c1OC)C(=O)C=CC2=O</td>\n",
       "      <td>Concentration</td>\n",
       "      <td>'='</td>\n",
       "      <td>...</td>\n",
       "      <td>CHEMBL1131845</td>\n",
       "      <td>1</td>\n",
       "      <td>Scientific Literature</td>\n",
       "      <td>Bioorg Med Chem Lett</td>\n",
       "      <td>1999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL1806550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.63</td>\n",
       "      <td>33</td>\n",
       "      <td>c1csc(-c2cc(-c3ccoc3)c3c(n2)-c2ccccc2OC3)c1</td>\n",
       "      <td>Inhibition</td>\n",
       "      <td>'='</td>\n",
       "      <td>...</td>\n",
       "      <td>CHEMBL1806416</td>\n",
       "      <td>1</td>\n",
       "      <td>Scientific Literature</td>\n",
       "      <td>Eur J Med Chem</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Molecule ChEMBL ID Molecule Name  Molecule Max Phase  Molecular Weight  \\\n",
       "0       CHEMBL540569           NaN                 NaN            500.90   \n",
       "1      CHEMBL3953305           NaN                 NaN            646.88   \n",
       "2        CHEMBL88605           NaN                 NaN            558.64   \n",
       "3        CHEMBL80138           NaN                 NaN            318.37   \n",
       "4      CHEMBL1806550           NaN                 NaN            331.40   \n",
       "\n",
       "   #RO5 Violations  AlogP Compound Key  \\\n",
       "0              0.0   0.71           7d   \n",
       "1              2.0   8.45            8   \n",
       "2              1.0   3.53          5an   \n",
       "3              0.0   3.25      table 2   \n",
       "4              1.0   5.63           33   \n",
       "\n",
       "                                              Smiles  Standard Type  \\\n",
       "0  CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2cc3c(NC(=O)CN)c...           IC50   \n",
       "1  C#CCOCCCCCCCCCCCCOc1ccc(-c2nc3ccc(-c4nc5ccc(N6...           IC50   \n",
       "2  CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2cc3cc(OC(=O)N4C...           IC50   \n",
       "3             CCCCCC(O)c1cc(OC)c2c(c1OC)C(=O)C=CC2=O  Concentration   \n",
       "4        c1csc(-c2cc(-c3ccoc3)c3c(n2)-c2ccccc2OC3)c1     Inhibition   \n",
       "\n",
       "  Standard Relation  ...  Document ChEMBL ID Source ID     Source Description  \\\n",
       "0               '='  ...       CHEMBL1126797         1  Scientific Literature   \n",
       "1               '>'  ...       CHEMBL4014329         1  Scientific Literature   \n",
       "2               '='  ...       CHEMBL1126797         1  Scientific Literature   \n",
       "3               '='  ...       CHEMBL1131845         1  Scientific Literature   \n",
       "4               '='  ...       CHEMBL1806416         1  Scientific Literature   \n",
       "\n",
       "       Document Journal Document Year Cell ChEMBL ID  Properties  Action Type  \\\n",
       "0            J Med Chem          1993  CHEMBL3307654         NaN          NaN   \n",
       "1            J Med Chem          2017            NaN         NaN          NaN   \n",
       "2            J Med Chem          1993  CHEMBL3307654         NaN          NaN   \n",
       "3  Bioorg Med Chem Lett          1999            NaN         NaN          NaN   \n",
       "4        Eur J Med Chem          2011            NaN         NaN          NaN   \n",
       "\n",
       "   Standard Text Value  Value  \n",
       "0                  NaN   0.26  \n",
       "1                  NaN  50.00  \n",
       "2                  NaN  21.00  \n",
       "3                  NaN   0.28  \n",
       "4                  NaN   0.00  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adc_db.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffac6875",
   "metadata": {},
   "source": [
    "Let's drop where missing values exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6c9e8f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "adc_db = adc_db.dropna(subset=['Smiles'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e2a0e0",
   "metadata": {},
   "source": [
    "Let's validate SMILES strings to ensure they are correct, as this will be required further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8e38c204",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_valid_smiles(smiles):\n",
    "    try:\n",
    "        return Chem.MolFromSmiles(smiles) is not None\n",
    "    except:\n",
    "        return False\n",
    "\n",
    "adc_db[\"valid_smiles\"] = adc_db[\"Smiles\"].apply(is_valid_smiles)\n",
    "adc_db = adc_db[adc_db[\"valid_smiles\"]].drop(columns=[\"valid_smiles\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c5eb7d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "adc_db['Activity'] = adc_db['Standard Value'].fillna(adc_db['Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0801e832",
   "metadata": {},
   "outputs": [],
   "source": [
    "adc_db = adc_db.dropna(subset=['Activity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a0444bc",
   "metadata": {},
   "source": [
    "In our ADC dataset, we compile a variety of physicochemical and biochemical properties essential for evaluating cytotoxic payloads. Some of them include IC50, which represents the concentration of a compound required to inhibit 50% of the target’s activity in an assay, typically expressed in nanomolar (nM) units. AlogP,is a lipophilicity estimate that must be balanced to ensure adequate cell permeability without excessive aggregation and avoid off‑target toxicity; Ro5 Violations, the number of Lipinski “Rule of Five” infractions that often predict poor solubility or bioavailability when too many are present; and molecular weight, since most ADC warheads fall within a tight size window and any outliers can flag potential synthesis or delivery challenges."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c71a56",
   "metadata": {},
   "source": [
    "In the context of ADC development, IC50 is critical for evaluating the potency of cytotoxic payloads. A lower IC50 value indicates higher potency, meaning the compound is effective at a lower concentration. We are using IC50 because it directly correlates with the cytotoxic potency required for ADC payloads. \n",
    "\n",
    "To gain deeper insights into the dataset, we will visualize the distribution of these properties, beginning with IC50 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ff92871a",
   "metadata": {},
   "outputs": [],
   "source": [
    "adc_db[\"IC50_nM\"] = pd.to_numeric(adc_db[\"Standard Value\"], errors=\"coerce\")\n",
    "adc_db = adc_db[adc_db[\"IC50_nM\"].notnull()].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c96dae1",
   "metadata": {},
   "source": [
    "Since IC50 values are typically expressed in nanomolar (nM) units for ADC payload analysis, let’s convert all IC50 measurements to nM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f082c3c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_nM(value, units):\n",
    "    if pd.isna(units) or pd.isna(value):\n",
    "        return value\n",
    "    units = units.strip().lower()\n",
    "    if units == 'um':\n",
    "        return value * 1_000\n",
    "    elif units == 'mm':\n",
    "        return value * 1_000_000\n",
    "    else:\n",
    "        return value\n",
    "\n",
    "adc_db['Activity_nM'] = adc_db.apply(\n",
    "    lambda row: convert_to_nM(row['Activity'], row['Standard Units'])\n",
    "    if row['Standard Type'] == 'IC50' else row['Activity'],\n",
    "    axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07384317",
   "metadata": {},
   "source": [
    "Before we proceed, let’s visualize the distribution of IC50 values to gain a better understanding of the potency profile in our payload dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7b8a08cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(adc_db[\"IC50_nM\"], bins=50, log=True)\n",
    "plt.xlabel(\"IC50 (nM)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Distribution of IC50 Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b50ed02",
   "metadata": {},
   "source": [
    "Let’s examine the distribution of IC50 values around the 0.2 nM range. Since there are outliers, we will apply log normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "228df8e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_ic50 = np.log10(adc_db[\"IC50_nM\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a36500a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_ic50 = np.array(log_ic50)\n",
    "log_ic50_clean = log_ic50[np.isfinite(log_ic50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e99cf447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(log_ic50_clean, bins=50)\n",
    "plt.xlabel(\"log10(IC50) (nM)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Distribution of log10(IC50) Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c615f9",
   "metadata": {},
   "source": [
    "Let's now examine the AlogP values, which estimate each payload's lipophilicity, a key driver of cell permeability, solubility, and off-target risks. A well-balanced log P helps ensure a payload can cross cell membranes without precipitating or sticking nonspecifically to proteins, minimizing aggregation and unintended toxicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0d62eb34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adc_db['AlogP'] = pd.to_numeric(adc_db['AlogP'], errors='coerce')\n",
    "\n",
    "adc_db = adc_db[adc_db['AlogP'].notnull()]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.histplot(adc_db['AlogP'], bins=30, kde=True, color='steelblue')\n",
    "plt.title('Distribution of AlogP Values in ADC Payloads')\n",
    "plt.xlabel('AlogP')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d6471c",
   "metadata": {},
   "source": [
    "As the distribution is centered around 5, not too high (which would indicate excessive lipophilicity and potential solubility issues) and not too low (which could suggest poor membrane permeability), this indicates that many payloads maintain a balanced lipophilic profile, making them suitable for cellular uptake."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e35f122",
   "metadata": {},
   "source": [
    "## References <a name=\"references\"></a>\n",
    "\n",
    "<a name=\"1\"></a> [1] Fu, Z., Li, S., Han, S., Shi, C., Zhang, Y., & Wang, Y. (2022). Antibody drug conjugate: the ‘biological missile’ for targeted cancer therapy. Signal Transduction and Targeted Therapy, 7(1), 93. https://www.nature.com/articles/s41392-022-00947-7\n",
    "\n",
    "<a name=\"2\"></a> [2] Maeda, Y., Yamaguchi, H., Fujii, K., et al. (2024). Antibody–Drug Conjugates: A Review of Approved Drugs and Their Clinical Pipeline. PMC, Current review. PMC10417123.\n",
    "\n",
    "<a name=\"3\"></a> [3] Antibody–drug conjugate. Wikipedia, The Free Encyclopedia, accessible at: https://en.wikipedia.org/wiki/Antibody–drug\\_conjugate.\n",
    "\n",
    "<a name=\"4\"></a> [4] Doronina, S.O., et al. (2021). Antibody–Drug Conjugates: Functional Principles and Preclinical Applications. Vaccines, 9(10), 1111.\n",
    "\n",
    "<a name=\"5\"></a> [5] Zeng, Z., Shao, X., Du, T., et al. (2025). Antibody–Drug Conjugates: Current Status and Future Perspectives. Journal of Hematology & Oncology, 18(1), 45. \n",
    "for\n",
    "<a name=\"6\"></a> [6] Bargh, J. D., Isidro‑Llobet, A., Parker, J. S., & Spring, D. R. (2021). Antibody–drug conjugates: Recent advances in linker chemistry. Chinese Journal of Cancer, review. https://pmc.ncbi.nlm.nih.gov/articles/PMC8727783/\n",
    "\n",
    "<a name=\"7\"></a> [7] Tsuchikama, K., & An, Z. (2016). Antibody‑drug conjugates: recent advances in conjugation and linker chemistries. Reviewed on ResearchGate and related platforms.\n",
    "\n",
    "<a name=\"8\"></a> [8] BOC Sciences. (n.d.). Analysis Method for Drug-to-Antibody Ratio (DAR) of Antibody–Drug Conjugates. https://www.bocsci.com/blog/analysis-method-for-drug-to-antibody-ratio-dar-of-antibody-drug-conjugates\n",
    "\n",
    "<a name=\"9\"></a> [9] Shen, L.T., Sun, X.N., Chen, Z., et al. (2024). ADCdb: the database of antibody‑drug conjugates. Nucleic Acids Research, 52(D1), D1097–D1109. [https://adcdb.idrblab.net](https://adcdb.idrblab.net)\n",
    "\n",
    "<a name=\"10\"></a> [10] Xu, S., & Xie, L. (2024). Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure. Retrieved data on Topoisomerase I inhibitors from the ChEMBL database (CHEMBL1781). https://www.ebi.ac.uk/chembl/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea6db999",
   "metadata": {},
   "source": [
    "# Congratulations! Time to join the Community!\n",
    "\n",
    "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c76089e",
   "metadata": {},
   "source": [
    "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
    "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82782b17",
   "metadata": {},
   "source": [
    "## Join the DeepChem Discord\n",
    "The DeepChem [Discord](https://discord.gg/cGzwCdrUqS) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104883b9",
   "metadata": {},
   "source": [
    "# Citing this tutorial\n",
    "If you found this tutorial useful please consider citing it using the provided BibTeX."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f97c37ce",
   "metadata": {},
   "source": [
    "```\n",
    "@manual{Molecular Machine Learning,\n",
    " title={Introduction to Antibody Drug Conjugates},\n",
    " organization={DeepChem},\n",
    " author={Patra, Sonali Lipsa and Bisoi, Ankita and Singh, Rakshit Kr. and Ramsundar, Bharath}\n",
    " howpublished = {\\url{https://github.com/deepchem/deepchem/blob/master/examples/tutorials/Introduction_to_Antibody_Drug_Conjugates.ipynb}},\n",
    " year={2025},\n",
    "}\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deepchem",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
